Empirical quantification of predictive uncertainty due to model discrepancy by training with an ensemble of experimental designs: an application to ion channel kinetics
DOI10.1007/s11538-023-01224-6zbMath1530.92079arXiv2302.02942MaRDI QIDQ6188373
Chon Lok Lei, Gary R. Mirams, Simon P. Preston, Joseph G. Shuttleworth, Adam P. Hill, Monique J. Windley, Dominic G. Whittaker
Publication date: 11 January 2024
Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2302.02942
discrepancymathematical modelmisspecificationuncertainty quantificationexperimental designion channel
Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) (92C45) Mathematical modeling or simulation for problems pertaining to biology (92-10) Experimental work for problems pertaining to biology (92-05)
Cites Work
- Unnamed Item
- Unnamed Item
- Towards a new evolutionary computation. Advances on estimation of distribution algorithms.
- Selected papers of Hirotugu Akaike
- A Bayesian nonparametric method for detecting rapid changes in disease transmission
- Bayesian Calibration of Computer Models
- Markov models for ion channels: versatility versus identifiability and speed
- Model Misspecification in Approximate Bayesian Computation: Consequences and Diagnostics
- Accounting for variability in ion current recordings using a mathematical model of artefacts in voltage-clamp experiments
- Considering discrepancy when calibrating a mechanistic electrophysiology model
- Calibration of Inexact Computer Models with Heteroscedastic Errors
This page was built for publication: Empirical quantification of predictive uncertainty due to model discrepancy by training with an ensemble of experimental designs: an application to ion channel kinetics